Embed2Detect：用于社交媒体事件检测的时间序列嵌入词
Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media
摘要：
社交媒体正在成为讨论世界各地正在发生的事情的主要媒介。因此，社交媒体平台生成的数据包含描述正在发生的事件的丰富信息。此外，与这些数据相关的及时性能够促进即时洞察。然而，考虑到社交媒体数据流中的动态性质和大量数据生产，手动过滤事件是不切实际的，因此，自动事件检测机制对社区来说是非常宝贵的。除了少数几个明显的例外，大多数以前的自动事件检测的研究只集中在统计和句法特征的数据，缺乏参与的潜在语义是重要的有效的信息检索从文本，因为他们代表的单词和它们的含义之间的连接。在本文中，我们提出了一种新的方法称为Embed 2Detect的社交媒体事件检测结合词嵌入和层次凝聚聚类的特点。词嵌入的采用使Embed 2Detect能够将强大的语义特征纳入事件检测，并克服了以前方法固有的主要限制。我们在最近两个代表体育和政治领域的真实社交媒体数据集上实验了我们的方法，并将结果与几种最先进的方法进行了比较。实验结果表明，Embed 2Detect能够有效地进行事件检测，其性能优于现有的事件检测方法。对于体育数据集，Embed 2Detect实现了比最佳基线高27%的F-测量，对于政治数据集，它增加了29%。
英文摘要：
Social media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
